In recent years, machine learning has been utilized to develop a number of robotic tools that mimic human sensing capability. For example, voice-to-text translation technologies enable hearing-impaired individuals to read what they cannot hear. Similarly, there exist automated narration technologies designed to assist vision-impaired individuals by conveying audio descriptions of what is captured with a camera. One major limitation with these technologies is that they lack the functionality to deliver the type of contextual inferences that humans naturally form instinctually when they process sensed information in context. For instance, vision-assist artificial intelligence technologies may be usable to strictly describe the imagery of a scene (e.g., what objects are present and where they are). If described well enough, the user may be able to form contextual inferences based on the received descriptions, but perhaps not without significant time listening to a lengthy description. Many common every-day tasks are onerous and difficult to perform without the ability to quickly act on contextual inferences.